The rapid development of social media, and special websites with critical reviews of products have created a huge collection of resources for customers all over the world. These data may contain a lot of information including product reviews, predicting market changes, and the polarity of opinions. Machine learning and deep learning algorithms provide the necessary tools for intelligence analysis in these challenges. In current competitive markets, it is essential to understand opinions, and sentiments of reviewers by extracting and analyzing their features. Besides, processing and analyzing this volume of data in the cloud can increase the cost of the system, strongly. Fewer dependencies on expensive hardware, storage space, and related software can be provided through cloud computing and Natural Language Processing (NLP). In our work, we propose an integrated architecture of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to identify the polarity of words on the Google cloud and performing computations on Google Colaboratory. Our proposed model based on deep learning algorithms with word embedding technique learns features through a CNN layer, and these features are fed directly into a bidirectional LSTM layer to capture long-term feature dependencies. Then, they can be reused from a CNN layer to provide abstract features before final dense layers. The main goal for this work is to provide an appropriate solution for analyzing sentiments and classification of the opinions into positive and negative classes. Our implementations show that found on the proposed model, the accuracy of more than 89.02% is achievable.

中文翻译：

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ConvLSTMConv网络：一种用于云计算中情感分析的深度学习方法

社交媒体的快速发展，以及带有对产品进行严格评论的特殊网站，为世界各地的客户创建了大量的资源。这些数据可能包含许多信息，包括产品评论，预测市场变化以及观点的极性。机器学习和深度学习算法为这些挑战中的情报分析提供了必要的工具。在当前竞争激烈的市场中，至关重要的是通过提取和分析评论者的特征来了解其观点和评论者的观点。此外，在云中处理和分析此数据量会极大地增加系统成本。通过云计算和自然语言处理（NLP），可以减少对昂贵硬件，存储空间和相关软件的依赖。在我们的工作中 我们提出了卷积神经网络（CNN）和长短期记忆（LSTM）网络的集成体系结构，以识别Google云上单词的极性并在Google合作实验室上执行计算。我们基于深度学习算法和词嵌入技术提出的模型通过CNN层学习特征，并将这些特征直接馈入双向LSTM层以捕获长期特征依赖性。然后，它们可以从CNN层重新使用，以在最终密集层之前提供抽象功能。这项工作的主要目的是提供一个适当的解决方案，以分析情绪并将意见分为正面和负面类别。我们的实现表明，在提出的模型上发现，可以达到89.02％以上的精度。